A device that measures weak biomagnetic signals is very susceptible to the influence of the strong magnetic interferences in its operational environment. This is due to the fact that compared to the biomagnetic signals being measured, the interference signals are even ten million times bigger. Furthermore, the implementation of the interference suppression is made more difficult because the region to be shielded from magnetic interferences is relatively large, tens of centimeters in its diameter.
To make biomagnetic measurements, several methods for protecting measuring devices from interference fields have been developed, which interference fields are many times larger than the interesting signals. In biomagnetic measurements, there is an attempt to achieve as good a signal-to-noise ratio as possible by placing the object being measured, e.g. a patient's head, as close as possible to the sensors of the measuring device while at the same time attenuating the interference sources using, for example, a suitable shielding solution and/or by processing the measured signals with computer algorithms. A straightforward method of shielding is to place a sensitive magnetic measuring device inside a so-called magnetically shielding room which suppresses magnetic fields originating from sources outside the room into about 100-10,000th part.
In addition to this, to achieve magnetic shielding, it is known to use sensors the geometrical structure of which makes them unsusceptible to rather steady magnetic fields originating from distant sources. Magnetic sensors of this kind are called gradiometers. Typically, a shielding factor of about 100-1,000 against external interferences is obtained using them. For example, publication EP0966689 discloses a magnetic gradiometer which is used to measure divergent components of the magnetic field. In particular, the apparatus as shown in publication EP0966689 is capable of measuring a small changing field irrespective of the earth's magnetic field (gradient component of the magnetic field).
Further, the magnetic shielding can be implemented, or it can be improved, using active systems in which the magnetic interference is eliminated by means of a suitable control system in which the interference is measured in the vicinity of the region being shielded by means of a sensor or sensors; and based on this measurement, the interference field is compensated with current-carrying coils that produce a magnetic field that is opposing with respect to the interference. Active magnetic shielding can be used either alone or combined with passive shielding methods such as a magnetic shielding room.
One efficient and dependable manner of eliminating interferences is a so-called SSS method (SSS=Signal Space Separation) because it can be used to separate biomagnetic signals from external interferences merely based on the basic physics of electromagnetic fields and on the geometry of the measuring device. The SSS method has been described e.g. in patent application WO2004081595 and in publication “Suppression of interference and artefacts by the signal space separation method”, Taulu et al, Brain Topography, Vol. 16, Number 4, pp. 269-275, 2004.
In the SSS method, a magnetic field measured by a multi-channel MEG device is analysed by examining three different volumes of the measurement geometry. The interesting source is in measurement volume V1; the sensors are in measurement volume V2 outside volume V1. The sources of magnetic interferences and the compensation coils are outside the aforementioned volumes in volume V3. In this examination, the V3 can also be infinite in volume. In the method, the magnetic field produced by the interesting sources disposed in volume V1 is parametrised in volume V2 as a sum of elementary fields, each of them being irrotational, sourceless and finite outside volume V1 so that a presentation of a desired accuracy is achieved for the parametrised magnetic field in volume V2. Similarly, the sum magnetic field produced by the interference fields and compensation coils disposed in volume V3 is parameterised in volume V2 as a sum of elementary fields. The measuring device's signal vectors corresponding to each elementary field are calculated. If a magnetic signal is measured using sensors, then thereafter, the fields produced from sources disposed in different volumes can be separated by calculating the components of the measured signal vector in the basis formed by the signal vectors associated with the elementary fields.
In certain biomagnetic measurements, the source of the interference signal can be disposed at a location where it cannot be classified as an external interference source based on geometric grounds. In that case, one necessitates additional information about the nature of the interference source, such as the exact form of the interference signal in the time domain in order to be able to model and possibly eliminate the interference from the measured data. Advance information required for a satisfactory outcome usually is difficult, or even impossible, to obtain. This kind of interference cannot be suppressed using passive shielding based on magnetic shielding structures, nor can it be suppressed using reference sensors measuring solely external interferences or by using gradiometers or by using the SSS method.
As an example of an application with interference sources disposed very close to the measuring sensors we mention the MEG measurements of such epilepsy patients having the Vagus Nerve Stimulator (VNS) installed for them. The stimulator in question is equipped with electrodes in the neck region that are activated electrically to reduce the number of epileptic fits. The principles of the VNS device have been described, for example, in publication “Vagus nerve stimulation for epilepsy: a review”, Binnie, Seizure, Vol. 9, pp. 161-169, 2000. For the time it takes to perform the MEG measurement, the stimulation of the vagus nerve is stopped, but even in that case, the VNS stimulator is activated periodically.
The prior-art technology has several ways of analysing and processing data sets computationally. One such method is the so-called Principal Component Analysis, PCA). The PCA has been used, for example, in publication US2005055175. The PCA enables one to reduce the dimensions of the data set while at the same time keeping as much as possible of the original information. Mutually correlating variables are modified into a set of uncorrelated variables that are sorted into an order. Uncorrelated variables are linear combinations of the original variables. The arranged variables to be obtained as a result are the desired main components.
Another method for processing a data set is the so-called ICA i.e. Independent Component Analysis). The ICA has been used in the prior art, for example, in publication US2005056140. The purpose of the ICA is to divide a complicated data set into independent data sets independent of one another. The ICA is a more efficient method than the PCA, and can be seen as an extension of the PCA. The ICA assumes that the data set to be examined is a linear or nonlinear combination of unknown variables. The way the combination is formed is unknown per se, but assuming that the variables are independent of each other it is possible to find out these unknown variables by means of the ICA.
A third known method for finding out the essential components in the time domain from the data is the use of the so-called Singular Value Decomposition (SVD). In the Singular Value Decomposition, the matrix M is decomposed according to the following equation:M=UΣV*  (1)
where M is an m*n-matrix whose elements are in region K. U is an m*m-dimensional unitary matrix in region K; V is an n*n-dimensional unitary matrix in region K; V* denotes the conjugate transpose of V; and Σ is an m*n-dimensional diagonal matrix whose diagonal elements are non-negative real numbers. In addition, it can also be required that the Σi,i of the diagonal elements must have been sorted into a descending order. In that case Z is unambiguously determined based on X, but U and V are not unambiguous.
The prior art has the disadvantage that the MEG measurements have not presented efficient and universally applicable methods for overcoming the aforementioned problems, which has until now shut certain patient groups out of magnetic measurements.